Traceability and provenance in big data medical systems

Richard McClatchey, Jetendr Shamdasani, Andrew Branson, Kamran Munir, Zsolt Kovacs, Giovanni Frisoni

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Providing an appropriate level of accessibility to and tracking of data or process elements in large volumes of medical data, is an essential requirement in the Big Data era. Researchers require systems that provide traceability of information through provenance data capture and management to support their clinical analyses. We present an approach that has been adopted in the neuGRID and N4U projects, which aimed to provide detailed traceability to support research analysis processes in the study of biomarkers for Alzheimer's disease, but is generically applicable across medical systems. To facilitate the orchestration of complex, large-scale analyses in these projects we have adapted CRISTAL, a workflow and provenance tracking solution. The use of CRISTAL has provided a rich environment for neuroscientists to track and manage the evolution of data and workflow usage over time in neuGRID and N4U.

Original languageEnglish
Title of host publicationProceedings - IEEE Symposium on Computer-Based Medical Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages226-231
Number of pages6
Volume2015-July
ISBN (Print)9781467367752
DOIs
Publication statusPublished - Jul 24 2015
Event28th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2015 - Sao Carlos and Ribeirao Preto, Brazil
Duration: Jun 22 2015Jun 25 2015

Other

Other28th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2015
CountryBrazil
CitySao Carlos and Ribeirao Preto
Period6/22/156/25/15

Keywords

  • Biomedical Analysis
  • Grid Computing
  • Neuroimaging
  • Provenance
  • Workflows

ASJC Scopus subject areas

  • Computer Science Applications
  • Radiology Nuclear Medicine and imaging

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